




import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


snake_case_  :      Optional[int]		=		logging.get_logger(__name__)


snake_case_  :      Dict		=		{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}

# See all LED models at https://huggingface.co/models?filter=LED
snake_case_  :      Tuple		=		{
    'vocab_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json',
    },
    'merges_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json',
    },
}

snake_case_  :      Tuple		=		{
    'allenai/led-base-16384': 16384,
}



@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def        __a							(  ) ->					int:
							"""simple docstring"""




							lowerCamelCase_			:    Dict   						=      (
							    list(range(ord("!"      )   ,		ord("~"      ) + 1      )      ) + list(range(ord("¡"      )   ,		ord("¬"      ) + 1      )      ) + list(range(ord("®"      )   ,		ord("ÿ"      ) + 1      )      )
							)
							lowerCamelCase_			:    str   						=      bs[:]
							lowerCamelCase_			:    Dict   						=      0
							for b in range(2**8      ):
														if b not in bs:
																					bs.append(__UpperCAmelCase      )
																					cs.append(2**8 + n      )
																					n += 1
							lowerCamelCase_			:    Tuple   						=      [chr(__UpperCAmelCase      ) for n in cs]
							return dict(zip(__UpperCAmelCase   ,		__UpperCAmelCase      )      )



def        __a							(  __UpperCAmelCase		:						Optional[int]      ) ->					str:
							"""simple docstring"""




							lowerCamelCase_			:    Optional[int]   						=      set()
							lowerCamelCase_			:    List[Any]   						=      word[0]
							for char in word[1:]:
														pairs.add((prev_char, char)      )
														lowerCamelCase_			:    Dict   						=      char
							return pairs





class 					snake_case_   (     snake_case_	):



				'''simple docstring'''
				lowerCamelCase       =  VOCAB_FILES_NAMES
				lowerCamelCase       =  PRETRAINED_VOCAB_FILES_MAP
				lowerCamelCase       =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowerCamelCase       =  ['input_ids', 'attention_mask']





				def __init__(      self							:   Optional[int]      ,							__magic_name__							:   Any      ,							__magic_name__							:   Optional[Any]      ,							__magic_name__							:   Optional[Any]="replace"      ,							__magic_name__							:   Optional[Any]="<s>"      ,							__magic_name__							:   Union[str, Any]="</s>"      ,							__magic_name__							:   List[Any]="</s>"      ,							__magic_name__							:   Any="<s>"      ,							__magic_name__							:   Optional[Any]="<unk>"      ,							__magic_name__							:   Dict="<pad>"      ,							__magic_name__							:   List[str]="<mask>"      ,							__magic_name__							:   Tuple=False      ,							**__magic_name__							:   Optional[int]      ,							)							->			Tuple:
											lowerCamelCase_			:    str   						=      AddedToken(lowerCAmelCase__      ,							lstrip=lowerCAmelCase__      ,							rstrip=lowerCAmelCase__							) if isinstance(lowerCAmelCase__      ,							lowerCAmelCase__							) else bos_token
											lowerCamelCase_			:    List[Any]   						=      AddedToken(lowerCAmelCase__      ,							lstrip=lowerCAmelCase__      ,							rstrip=lowerCAmelCase__							) if isinstance(lowerCAmelCase__      ,							lowerCAmelCase__							) else eos_token
											lowerCamelCase_			:    Tuple   						=      AddedToken(lowerCAmelCase__      ,							lstrip=lowerCAmelCase__      ,							rstrip=lowerCAmelCase__							) if isinstance(lowerCAmelCase__      ,							lowerCAmelCase__							) else sep_token
											lowerCamelCase_			:    Any   						=      AddedToken(lowerCAmelCase__      ,							lstrip=lowerCAmelCase__      ,							rstrip=lowerCAmelCase__							) if isinstance(lowerCAmelCase__      ,							lowerCAmelCase__							) else cls_token
											lowerCamelCase_			:    Union[str, Any]   						=      AddedToken(lowerCAmelCase__      ,							lstrip=lowerCAmelCase__      ,							rstrip=lowerCAmelCase__							) if isinstance(lowerCAmelCase__      ,							lowerCAmelCase__							) else unk_token
											lowerCamelCase_			:    int   						=      AddedToken(lowerCAmelCase__      ,							lstrip=lowerCAmelCase__      ,							rstrip=lowerCAmelCase__							) if isinstance(lowerCAmelCase__      ,							lowerCAmelCase__							) else pad_token

											# Mask token behave like a normal word, i.e. include the space before it
											lowerCamelCase_			:    Any   						=      AddedToken(lowerCAmelCase__      ,							lstrip=lowerCAmelCase__      ,							rstrip=lowerCAmelCase__							) if isinstance(lowerCAmelCase__      ,							lowerCAmelCase__							) else mask_token

											super().__init__(
											    errors=lowerCAmelCase__      ,							bos_token=lowerCAmelCase__      ,							eos_token=lowerCAmelCase__      ,							unk_token=lowerCAmelCase__      ,							sep_token=lowerCAmelCase__      ,							cls_token=lowerCAmelCase__      ,							pad_token=lowerCAmelCase__      ,							mask_token=lowerCAmelCase__      ,							add_prefix_space=lowerCAmelCase__      ,							**lowerCAmelCase__      ,							)

											with open(lowerCAmelCase__      ,							encoding="utf-8"							) as vocab_handle:
																		lowerCamelCase_			:    str   						=      json.load(lowerCAmelCase__							)
											lowerCamelCase_			:    str   						=      {v: k for k, v in self.encoder.items()}
											lowerCamelCase_			:    Union[str, Any]   						=      errors  # how to handle errors in decoding
											lowerCamelCase_			:    Union[str, Any]   						=      bytes_to_unicode()
											lowerCamelCase_			:    List[Any]   						=      {v: k for k, v in self.byte_encoder.items()}
											with open(lowerCAmelCase__      ,							encoding="utf-8"							) as merges_handle:
																		lowerCamelCase_			:    Union[str, Any]   						=      merges_handle.read().split("\n"							)[1:-1]
											lowerCamelCase_			:    List[Any]   						=      [tuple(merge.split()							) for merge in bpe_merges]
											lowerCamelCase_			:    Union[str, Any]   						=      dict(zip(lowerCAmelCase__      ,							range(len(lowerCAmelCase__							)							)							)							)
											lowerCamelCase_			:    Optional[int]   						=      {}
											lowerCamelCase_			:    Dict   						=      add_prefix_space

											# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
											lowerCamelCase_			:    Tuple   						=      re.compile(R"\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"							)





				@property
				# Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
				def    __SCREAMING_SNAKE_CASE     (      self							:   Optional[int]							)							->			str:
											return len(self.encoder							)





				def    __SCREAMING_SNAKE_CASE     (      self							:   Tuple							)							->			str:
											return dict(self.encoder      ,							**self.added_tokens_encoder							)





				def    __SCREAMING_SNAKE_CASE     (      self							:   Optional[int]      ,							__magic_name__							:   List[Any]							)							->			Union[str, Any]:
											if token in self.cache:
																		return self.cache[token]
											lowerCamelCase_			:    Union[str, Any]   						=      tuple(lowerCAmelCase__							)
											lowerCamelCase_			:    List[str]   						=      get_pairs(lowerCAmelCase__							)

											if not pairs:
																		return token

											while True:
																		lowerCamelCase_			:    List[Any]   						=      min(lowerCAmelCase__      ,							key=lambda __magic_name__							: self.bpe_ranks.get(lowerCAmelCase__      ,							float("inf"							)							)							)
																		if bigram not in self.bpe_ranks:
																									break
																		lowerCamelCase_			:    Optional[int]   						=      bigram
																		lowerCamelCase_			:    str   						=      []
																		lowerCamelCase_			:    Any   						=      0
																		while i < len(lowerCAmelCase__							):
																									try:
																																lowerCamelCase_			:    Tuple   						=      word.index(lowerCAmelCase__      ,							lowerCAmelCase__							)
																									except ValueError:
																																new_word.extend(word[i:]							)
																																break
																									else:
																																new_word.extend(word[i:j]							)
																																lowerCamelCase_			:    List[Any]   						=      j

																									if word[i] == first and i < len(lowerCAmelCase__							) - 1 and word[i + 1] == second:
																																new_word.append(first + second							)
																																i += 2
																									else:
																																new_word.append(word[i]							)
																																i += 1
																		lowerCamelCase_			:    List[Any]   						=      tuple(lowerCAmelCase__							)
																		lowerCamelCase_			:    Union[str, Any]   						=      new_word
																		if len(lowerCAmelCase__							) == 1:
																									break
																		else:
																									lowerCamelCase_			:    Any   						=      get_pairs(lowerCAmelCase__							)
											lowerCamelCase_			:    int   						=      ''' '''.join(lowerCAmelCase__							)
											lowerCamelCase_			:    List[str]   						=      word
											return word





				def    __SCREAMING_SNAKE_CASE     (      self							:   List[str]      ,							__magic_name__							:   Dict							)							->			int:
											lowerCamelCase_			:    List[Any]   						=      []
											for token in re.findall(self.pat      ,							lowerCAmelCase__							):
																		lowerCamelCase_			:    Optional[int]   						=      ''''''.join(
																		    self.byte_encoder[b] for b in token.encode("utf-8"							)							)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
																		bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowerCAmelCase__							).split(" "							)							)
											return bpe_tokens





				def    __SCREAMING_SNAKE_CASE     (      self							:   int      ,							__magic_name__							:   List[str]							)							->			Optional[Any]:
											return self.encoder.get(lowerCAmelCase__      ,							self.encoder.get(self.unk_token							)							)





				def    __SCREAMING_SNAKE_CASE     (      self							:   str      ,							__magic_name__							:   Tuple							)							->			Tuple:
											return self.decoder.get(lowerCAmelCase__							)





				def    __SCREAMING_SNAKE_CASE     (      self							:   List[Any]      ,							__magic_name__							:   Tuple							)							->			List[Any]:
											lowerCamelCase_			:    int   						=      ''''''.join(lowerCAmelCase__							)
											lowerCamelCase_			:    str   						=      bytearray([self.byte_decoder[c] for c in text]							).decode("utf-8"      ,							errors=self.errors							)
											return text





				def    __SCREAMING_SNAKE_CASE     (      self							:   List[str]      ,							__magic_name__							:   str      ,							__magic_name__							:   Optional[str] = None							)							->			Dict:
											if not os.path.isdir(lowerCAmelCase__							):
																		logger.error(F"Vocabulary path ({save_directory}) should be a directory"							)
																		return
											lowerCamelCase_			:    Tuple   						=      os.path.join(
											    lowerCAmelCase__      ,							(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]							)
											lowerCamelCase_			:    Tuple   						=      os.path.join(
											    lowerCAmelCase__      ,							(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]							)

											with open(lowerCAmelCase__      ,							"w"      ,							encoding="utf-8"							) as f:
																		f.write(json.dumps(self.encoder      ,							indent=2      ,							sort_keys=lowerCAmelCase__      ,							ensure_ascii=lowerCAmelCase__							) + "\n"							)

											lowerCamelCase_			:    str   						=      0
											with open(lowerCAmelCase__      ,							"w"      ,							encoding="utf-8"							) as writer:
																		writer.write("#version: 0.2\n"							)
																		for bpe_tokens, token_index in sorted(self.bpe_ranks.items()      ,							key=lambda __magic_name__							: kv[1]							):
																									if index != token_index:
																																logger.warning(
																																    F"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
																																    " Please check that the tokenizer is not corrupted!"							)
																																lowerCamelCase_			:    Optional[Any]   						=      token_index
																									writer.write(" ".join(lowerCAmelCase__							) + "\n"							)
																									index += 1

											return vocab_file, merge_file





				def    __SCREAMING_SNAKE_CASE     (      self							:   List[Any]      ,							__magic_name__							:   List[int]      ,							__magic_name__							:   Optional[List[int]] = None							)							->			Tuple:
											if token_ids_a is None:
																		return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
											lowerCamelCase_			:    Dict   						=      [self.cls_token_id]
											lowerCamelCase_			:    Optional[int]   						=      [self.sep_token_id]
											return cls + token_ids_a + sep + sep + token_ids_a + sep





				def    __SCREAMING_SNAKE_CASE     (      self							:   str      ,							__magic_name__							:   List[int]      ,							__magic_name__							:   Optional[List[int]] = None      ,							__magic_name__							:   bool = False							)							->			List[str]:
											if already_has_special_tokens:
																		return super().get_special_tokens_mask(
																		    token_ids_a=lowerCAmelCase__      ,							token_ids_a=lowerCAmelCase__      ,							already_has_special_tokens=lowerCAmelCase__							)

											if token_ids_a is None:
																		return [1] + ([0] * len(lowerCAmelCase__							)) + [1]
											return [1] + ([0] * len(lowerCAmelCase__							)) + [1, 1] + ([0] * len(lowerCAmelCase__							)) + [1]





				def    __SCREAMING_SNAKE_CASE     (      self							:   Dict      ,							__magic_name__							:   List[int]      ,							__magic_name__							:   Optional[List[int]] = None							)							->			List[Any]:
											lowerCamelCase_			:    str   						=      [self.sep_token_id]
											lowerCamelCase_			:    Union[str, Any]   						=      [self.cls_token_id]

											if token_ids_a is None:
																		return len(cls + token_ids_a + sep							) * [0]
											return len(cls + token_ids_a + sep + sep + token_ids_a + sep							) * [0]





				def    __SCREAMING_SNAKE_CASE     (      self							:   int      ,							__magic_name__							:   List[Any]      ,							__magic_name__							:   Any=False      ,							**__magic_name__							:   Any							)							->			int:
											lowerCamelCase_			:    Tuple   						=      kwargs.pop("add_prefix_space"      ,							self.add_prefix_space							)
											if (is_split_into_words or add_prefix_space) and (len(lowerCAmelCase__							) > 0 and not text[0].isspace()):
																		lowerCamelCase_			:    Any   						=      ''' ''' + text
											return (text, kwargs)





				def    __SCREAMING_SNAKE_CASE     (      self							:   Dict      ,							__magic_name__							:   Union[Dict[str, EncodedInput], BatchEncoding]      ,							__magic_name__							:   Optional[int] = None      ,							__magic_name__							:   PaddingStrategy = PaddingStrategy.DO_NOT_PAD      ,							__magic_name__							:   Optional[int] = None      ,							__magic_name__							:   Optional[bool] = None      ,							)							->			List[Any]:
											lowerCamelCase_			:    Dict   						=      super()._pad(
											    encoded_inputs=lowerCAmelCase__      ,							max_length=lowerCAmelCase__      ,							padding_strategy=lowerCAmelCase__      ,							pad_to_multiple_of=lowerCAmelCase__      ,							return_attention_mask=lowerCAmelCase__      ,							)

											# Load from model defaults
											if return_attention_mask is None:
																		lowerCamelCase_			:    int   						=      '''attention_mask''' in self.model_input_names

											if return_attention_mask and "global_attention_mask" in encoded_inputs:
																		lowerCamelCase_			:    Tuple   						=      encoded_inputs[self.model_input_names[0]]
																		# `global_attention_mask` need to have the same length as other (sequential) inputs.
																		lowerCamelCase_			:    int   						=      len(encoded_inputs["global_attention_mask"]							) != len(lowerCAmelCase__							)

																		if needs_to_be_padded:
																									lowerCamelCase_			:    Dict   						=      len(lowerCAmelCase__							) - len(encoded_inputs["global_attention_mask"]							)

																									if self.padding_side == "right":
																																# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
																																lowerCamelCase_			:    Optional[int]   						=      (
																																    encoded_inputs['''global_attention_mask'''] + [-1] * difference
																																)
																									elif self.padding_side == "left":
																																lowerCamelCase_			:    Any   						=      [-1] * difference + encoded_inputs[
																																    '''global_attention_mask'''
																																]
																									else:
																																raise ValueError("Invalid padding strategy:" + str(self.padding_side							)							)

											return encoded_inputs






'''simple docstring'''



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()
class 						lowerCamelCase_				(       snake_case_     ,					unittest.TestCase      ):
						_lowerCAmelCase  : str						  =					KandinskyInpaintPipeline
						_lowerCAmelCase  : List[Any]						  =					['prompt', 'image_embeds', 'negative_image_embeds', 'image', 'mask_image']
						_lowerCAmelCase  : int						  =					[
						    'prompt',
						    'negative_prompt',
						    'image_embeds',
						    'negative_image_embeds',
						    'image',
						    'mask_image',
						]
						_lowerCAmelCase  : List[str]						  =					[
						    'generator',
						    'height',
						    'width',
						    'latents',
						    'guidance_scale',
						    'negative_prompt',
						    'num_inference_steps',
						    'return_dict',
						    'guidance_scale',
						    'num_images_per_prompt',
						    'output_type',
						    'return_dict',
						]
						_lowerCAmelCase  : Union[str, Any]						  =					False







						@property
						def        __lowercase     (	self :   str					):

													"""simple docstring"""
													return 32







						@property
						def        __lowercase     (	self :   str					):

													"""simple docstring"""
													return 32







						@property
						def        __lowercase     (	self :   Optional[int]					):

													"""simple docstring"""
													return self.time_input_dim







						@property
						def        __lowercase     (	self :   int					):

													"""simple docstring"""
													return self.time_input_dim * 4







						@property
						def        __lowercase     (	self :   int					):

													"""simple docstring"""
													return 1_00







						@property
						def        __lowercase     (	self :   Tuple					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''					)
													return tokenizer







						@property
						def        __lowercase     (	self :   List[Any]					):

													"""simple docstring"""
													torch.manual_seed(0					)
													SCREAMING_SNAKE_CASE       :   List[Any]         =      MCLIPConfig(
													    numDims=self.cross_attention_dim					,     transformerDimensions=self.text_embedder_hidden_size					,     hidden_size=self.text_embedder_hidden_size					,     intermediate_size=37					,     num_attention_heads=4					,     num_hidden_layers=5					,     vocab_size=10_05					,     )

													SCREAMING_SNAKE_CASE       :   Any         =      MultilingualCLIP(lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      text_encoder.eval()

													return text_encoder







						@property
						def        __lowercase     (	self :   List[Any]					):

													"""simple docstring"""
													torch.manual_seed(0					)

													SCREAMING_SNAKE_CASE       :   Tuple         =      {
													    '''in_channels''': 9,
													    # Out channels is double in channels because predicts mean and variance
													    '''out_channels''': 8,
													    '''addition_embed_type''': '''text_image''',
													    '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
													    '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
													    '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
													    '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
													    '''layers_per_block''': 1,
													    '''encoder_hid_dim''': self.text_embedder_hidden_size,
													    '''encoder_hid_dim_type''': '''text_image_proj''',
													    '''cross_attention_dim''': self.cross_attention_dim,
													    '''attention_head_dim''': 4,
													    '''resnet_time_scale_shift''': '''scale_shift''',
													    '''class_embed_type''': None,
													}

													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      UNetaDConditionModel(**lowerCAmelCase__					)
													return model







						@property
						def        __lowercase     (	self :   Optional[Any]					):

													"""simple docstring"""
													return {
													    "block_out_channels": [32, 64],
													    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
													    "in_channels": 3,
													    "latent_channels": 4,
													    "layers_per_block": 1,
													    "norm_num_groups": 8,
													    "norm_type": "spatial",
													    "num_vq_embeddings": 12,
													    "out_channels": 3,
													    "up_block_types": [
													        "AttnUpDecoderBlock2D",
													        "UpDecoderBlock2D",
													    ],
													    "vq_embed_dim": 4,
													}







						@property
						def        __lowercase     (	self :   Union[str, Any]					):

													"""simple docstring"""
													torch.manual_seed(0					)
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      VQModel(**self.dummy_movq_kwargs					)
													return model







						def        __lowercase     (	self :   Optional[int]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Any         =      self.dummy_text_encoder
													SCREAMING_SNAKE_CASE       :   Tuple         =      self.dummy_tokenizer
													SCREAMING_SNAKE_CASE       :   int         =      self.dummy_unet
													SCREAMING_SNAKE_CASE       :   str         =      self.dummy_movq

													SCREAMING_SNAKE_CASE       :   Optional[int]         =      DDIMScheduler(
													    num_train_timesteps=10_00					,     beta_schedule='''linear'''					,     beta_start=0.0_0085					,     beta_end=0.012					,     clip_sample=lowerCAmelCase__					,     set_alpha_to_one=lowerCAmelCase__					,     steps_offset=1					,     prediction_type='''epsilon'''					,     thresholding=lowerCAmelCase__					,     )

													SCREAMING_SNAKE_CASE       :   Optional[int]         =      {
													    '''text_encoder''': text_encoder,
													    '''tokenizer''': tokenizer,
													    '''unet''': unet,
													    '''scheduler''': scheduler,
													    '''movq''': movq,
													}

													return components







						def        __lowercase     (	self :   Dict					,     lowerCAmelCase__ :   Optional[Any]					,     lowerCAmelCase__ :   List[Any]=0					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      floats_tensor((1, self.cross_attention_dim)					,     rng=random.Random(lowerCAmelCase__					)					).to(lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   List[Any]         =      floats_tensor((1, self.cross_attention_dim)					,     rng=random.Random(seed + 1					)					).to(lowerCAmelCase__					)
													# create init_image
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      floats_tensor((1, 3, 64, 64)					,     rng=random.Random(lowerCAmelCase__					)					).to(lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   Tuple         =      image.cpu().permute(0					,     2					,     3					,     1					)[0]
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      Image.fromarray(np.uinta(lowerCAmelCase__					)					).convert('''RGB'''					).resize((2_56, 2_56)					)
													# create mask
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      np.ones((64, 64)					,     dtype=np.floataa					)
													SCREAMING_SNAKE_CASE       :   str         =      0

													if str(lowerCAmelCase__					).startswith('''mps'''					):
																				SCREAMING_SNAKE_CASE       :   Tuple         =      torch.manual_seed(lowerCAmelCase__					)
													else:
																				SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      torch.Generator(device=lowerCAmelCase__					).manual_seed(lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   Tuple         =      {
													    '''prompt''': '''horse''',
													    '''image''': init_image,
													    '''mask_image''': mask,
													    '''image_embeds''': image_embeds,
													    '''negative_image_embeds''': negative_image_embeds,
													    '''generator''': generator,
													    '''height''': 64,
													    '''width''': 64,
													    '''num_inference_steps''': 2,
													    '''guidance_scale''': 4.0,
													    '''output_type''': '''np''',
													}
													return inputs







						def        __lowercase     (	self :   List[Any]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Any         =      '''cpu'''

													SCREAMING_SNAKE_CASE       :   Dict         =      self.get_dummy_components()

													SCREAMING_SNAKE_CASE       :   Optional[int]         =      self.pipeline_class(**lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   int         =      pipe.to(lowerCAmelCase__					)

													pipe.set_progress_bar_config(disable=lowerCAmelCase__					)

													SCREAMING_SNAKE_CASE       :   str         =      pipe(**self.get_dummy_inputs(lowerCAmelCase__					)					)
													SCREAMING_SNAKE_CASE       :   int         =      output.images

													SCREAMING_SNAKE_CASE       :   int         =      pipe(
													    **self.get_dummy_inputs(lowerCAmelCase__					)					,     return_dict=lowerCAmelCase__					,     )[0]

													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      image[0, -3:, -3:, -1]
													SCREAMING_SNAKE_CASE       :   Dict         =      image_from_tuple[0, -3:, -3:, -1]

													print(F"""image.shape {image.shape}"""					)

													assert image.shape == (1, 64, 64, 3)

													SCREAMING_SNAKE_CASE       :   List[str]         =      np.array(
													    [0.832_6919, 0.7379_0467, 0.2091_8581, 0.930_9612, 0.551_1791, 0.4371_3328, 0.551_3321, 0.4992_2934, 0.5949_7786]					)

													assert (
													    np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2
													), F""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
													assert (
													    np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() < 1e-2
													), F""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""







						def        __lowercase     (	self :   str					):

													"""simple docstring"""
													super().test_inference_batch_single_identical(expected_max_diff=3e-3					)


@slow
@require_torch_gpu
class 						lowerCamelCase_				(       unittest.TestCase      ):







						def        __lowercase     (	self :   List[str]					):

													"""simple docstring"""
													# clean up the VRAM after each test
													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()







						def        __lowercase     (	self :   Dict					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   List[Any]         =      load_numpy(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
													    '''/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'''					)

													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      load_image(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''					)
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      np.ones((7_68, 7_68)					,     dtype=np.floataa					)
													SCREAMING_SNAKE_CASE       :   List[str]         =      0

													SCREAMING_SNAKE_CASE       :   Dict         =      '''a hat'''

													SCREAMING_SNAKE_CASE       :   str         =      KandinskyPriorPipeline.from_pretrained(
													    '''kandinsky-community/kandinsky-2-1-prior'''					,     torch_dtype=torch.floataa					)
													pipe_prior.to(lowerCAmelCase__					)

													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      KandinskyInpaintPipeline.from_pretrained(
													    '''kandinsky-community/kandinsky-2-1-inpaint'''					,     torch_dtype=torch.floataa					)
													SCREAMING_SNAKE_CASE       :   Dict         =      pipeline.to(lowerCAmelCase__					)
													pipeline.set_progress_bar_config(disable=lowerCAmelCase__					)

													SCREAMING_SNAKE_CASE       :   Optional[int]         =      torch.Generator(device='''cpu'''					).manual_seed(0					)
													SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   Any         =      pipe_prior(
													    lowerCAmelCase__					,     generator=lowerCAmelCase__					,     num_inference_steps=5					,     negative_prompt=''''''					,     ).to_tuple()

													SCREAMING_SNAKE_CASE       :   Tuple         =      pipeline(
													    lowerCAmelCase__					,     image=lowerCAmelCase__					,     mask_image=lowerCAmelCase__					,     image_embeds=lowerCAmelCase__					,     negative_image_embeds=lowerCAmelCase__					,     generator=lowerCAmelCase__					,     num_inference_steps=1_00					,     height=7_68					,     width=7_68					,     output_type='''np'''					,     )

													SCREAMING_SNAKE_CASE       :   Tuple         =      output.images[0]

													assert image.shape == (7_68, 7_68, 3)

													assert_mean_pixel_difference(lowerCAmelCase__					,     lowerCAmelCase__					)




